Wavelets for images: Extensive prior research has uncovered and exploited various multi-resolution analysis properties of wavelet transforms on image processing applications, including denoising [1]}, super-resolution [2]}, recognition [3]}, and compression [4]}. Features extracted using wavelet transforms have also been used extensively with machine learning models [5]}, such as support vector machines and neural networks [6]}, especially for image classification [7]}. Representative instances of integration with neural architectures include the following. ScatNet architecture cascades wavelet transform layers with nonlinear modulus and average pooling to extract translation invariant features that are robust to deformations and preserves high-frequency information for image classification [8]}. WaveCNets replaces max-pooling, strided-convolution, and average-pooling of CNNs with 2D-DWT for noise-robust image classification [9]}. Multi-level wavelet CNN (MWCNN) has been used for image restoration as well with U-Net architectures for better trade-off between receptive field size and computational efficiency [10]}. Wavelet transform has also been combined with a fully convolutional neural network for image super resolution  [11]}.
